A COMPUTATIONAL FRAMEWORK FOR DISCRIMINATIVE ANALYSIS OF HIGH DIMENSIONAL BIOMEDICAL IMAGE DATA
Open Access
Author:
Kashyap, Somesh
Graduate Program:
Electrical Engineering
Degree:
Master of Science
Document Type:
Master Thesis
Date of Defense:
April 02, 2010
Committee Members:
Yanxi Liu, Thesis Advisor/Co-Advisor Yanxi Liu, Thesis Advisor/Co-Advisor Robert Collins, Thesis Advisor/Co-Advisor David Miller, Thesis Advisor/Co-Advisor
Keywords:
Expression Machine learning Plant 3D Face Age Brain Asymmetry Gender Computer-Aided Diagnosis Alzheimer Pipeline
Abstract:
In this work we propose, implement and evaluate a discriminative computational pipeline to address the problem of feature generation, feature screening and feature subset selection from biomedical datasets when the ratio of feature dimensions (possibly in millions) to number of samples is very high . The proposed pipeline is modular and can be highly parallel. The framework is applied to a variety of real world discrimination problems including plant species classification from 2D leaf images, Gender/age/expression/human identification from 3D facial surface mesh and Gender/Age/Disease classification from 3D neural Magnetic Resonance Imaging (MRI) images. By using a unique set of novel features for each application we either achieve better or competitive classification accuracy than existing work or set new benchmarks otherwise. We can also locate discriminative regions for faces and brains which facilitate further scientific discoveries. This work illustrates quantitatively, the effectiveness and the diversity of the proposed feature extraction and machine learning pipelines.
